The subject matter disclosed herein relates generally to a power curve correlation system. Specifically, the subject matter disclosed herein relates to a system for correlating and organizing the load data of a power supply system (e.g., power grid, smart grid, etc.), to provide more accurate load forecast models.
Load forecast models aid power supply companies in maintaining proper operation and accurately anticipating future power demands. More specifically, load forecast models can provide anticipated power usages for given times of day, year, etc., that are provided by a power supply system. Power supply companies rely on forecasting models to determine whether anticipatory measures are necessary in order to maintain a power supply to users. Generally, power system engineers (PSE) verify the accuracy of conventional load forecast models by reviewing large amounts of historical load data and comparing the historical data to the conventionally created load forecast models. The power system engineers must completely understand the historical load data in order to verify the accuracy of the load forecast model. As a result, the power system engineers spend much of their time reviewing the load forecast model and comparing the historical load data and the forecasting results. These large data quantities and extensive review times may lead to the development and persistence of inaccuracies in the conventional load forecast models. More specifically, the power system engineer does not have sufficient time to correlate, categorize and understand the large amount of historical data prior to verifying the accuracy of the load forecast models. If all of the historical data is not correlated, categorized and understood, inaccuracies in the load forecast model will not be identified, and the load forecast model will be defective. When a defective load forecast model does not accurately predict a necessary power network load, the power network's load supply is inadequate and will result in a power network problem (e.g., failure, power outages, etc.). Power supply companies rely on load forecasting models to accurately predict the necessary power network loads in order to avoid power network problems.